Cancer biomarkers can be used for developing assays for clinical diagnosis, identifying patient’s response to a particular drug, optimizing personalized drug treatment regimen (drug dose, drug treatment schedule etc.), monitoring the efficacy of treatment (disease stage, tumor progression, tumor recurrence etc.) and in cancer theranostics. With the growing trend towards the advancement of personalized medicine concept, companion diagnostic tools may play a significant role in patient stratification by identifying patients with positive clinical response to an existing or novel treatment method. However, current limitations in identifying life-threatening side effects of therapeutic drugs may have negative impact on developing efficient drug therapy strategies, often difficult to identify short or long term side effects of drugs during clinical trials. Therefore, there is a need for developing predictive methods and assays for identifying secondary disease causing side effects of drugs. We propose disease specific diagnostic biomarkers as an attractive tool for predicting the occurrence of secondary diseases from a specific drug treatment method. In this blog, we tried to explore the potential of cancer diagnostic biomarkers for predicting therapeutic drug (non anti-cancer drugs) induced cancer occurrence in patients. For identifying biomolecules that might be potentially associated with pioglitazone induced bladder cancer development in patients, hypothesis driven functional integration and identification of biomolecules, incorporating traditional pathway analysis, linked to bladder cancer specific diagnostic biomarkers and drug target (PPARgamma) were adopted. Link to the full blog article: Cancer Biomarker Strategy to Develop Companion Diagnostics for Predicting Prescription Drug Induced Tumors – Analysis using pioglitazone (Actos) and bladder cancer

Following are some of the companion diagnostics or theranostics products that are currently available (may not be available worldwide), in addition to the US FDA approved Genetech’s Hercept® with DakoCytomation’s HercepTest® for breast cancer and Roche’s Zelboraf ® and companion diagnostic test for late-stage skin cancer.

Availability of a comprehensive cancer biomarker database may opens up scientific and technical opportunities in developing innovative oncologic theranostics (Rx/Dx), a diagnostic therapy process that leads to the development of successful personalized medicine strategies in cancer treatment.

With the growing trend towards the advancement of personalized medicine concept, there is a need to develop strategies and tools that can be used for individualized diagnosis and treatment. Theranostics based tools, a combination of diagnostics and therapeutics approach, offer promising agents that can be used for the improved diagnosis and treatment of various diseases. In oncologic theranostics, developing innovative personalized cancer treatment rely on the identification of novel cancer biomarkers and diagnostic assays to identify patient’s response to a particular drug, for optimizing personalized drug treatment regimen (drug dose, drug treatment schedule etc) and for monitoring the efficacy of treatment (disease stage, tumor progression, tumor recurrence etc). A best example for this will be the approval of Genentech’s Herceptin® with DakoCytomation’s HercepTest® for breast cancer theranostics. Future development of cancer theranostic tools depends on the discovery and validation of existing or novel cancer biomarkers. A database that contains comprehensive information on discovery phase or clinically validated biomarkers, along with therapeutic drug target information, can be a powerful tool in developing novel theranostic assays as well as for the discovery of new drug targets based on theranostics (Fig.1). A combination of therapeutic drug target and biomarker pathway analysis, in particular companion diagnostics pathways, can pave the path towards developing innovative strategies in cancer theranostics.

Biomarkers that have potential applications in cancer theranostics can be broadly classified into:

1. Imaging biomarkers: Drug molecules labeled with imaging tags (e.g. NRI, MRI etc.) and antigen-directed imaging drugs (e.g. radiolabeled antibody drugs) are the very good examples. In this case, a single molecule can be used as a diagnostic and therapeutic agent, EGFR, VEGF and TAG-72 are very good examples where antibodies against these drug targets tagged with imaging markers can be used in theranostics. Imaging biomarkers can also be very useful in targeted surgical treatment of cancer. Labeled antibody based detection of phosphorylated or dephosphorylated will be an attractive theranostics tool in phosphorylation-dependent targeted cancer therapy and diagnosis. Epigenetic biomarkers are another attractive target for developing cancer theranostics. Applications of additional tools such as nanoparticles and gold particles have been demonstrated in theranostics.

2. Diagnostic/prognostic protein biomarkers: Immunohisotchemistry and immunoassays (e.g. ELISA) can be used in theranostic applications. Identification of diagnostic biomarkers that can be used as therapeutic drug targets will have significant impact in theranostics. Development of protein biomarker-directed antibodies (labeled) or small molecules or aptamers can be a potential theranostics tool.

3. Molecular diagnostic markers (genes/SNPs/miRNA/epigenetic): PCR, qPCR, DNA sequencing (including next generation sequencing), and microarray based technologies can be used as theranostics tools. Single step diagnostic therapy, like labeled antibody drug based theraostics, may be a challenging task with molecular diagnostic biomarkers, possible exceptions are siRNA or miRNA based cancer therapies.

4. Cell based biomarkers: Cancer stem cells, circulating tumor cells (CTCs) and tumor-infiltrating immune cells (CD68-positive macrophages/T-cells etc.) can be used in cancer theranostics. The diagnostic and therapeutic significance of these cell based biomarkers have been demonstrated in several published studies.

5. Drug efficacy/response/predictive biomarkers: Biomarkers include proteins, gene, miRNA, SNPs, metabolites etc., which can be successfully used for the development of companion diagnostic assays. These biomarkers can also become a therapeutic drug target for further discovery of theranostics based therapeutic drug targets.

6. Combination therapy response biomarkers: Combination therapy approaches have been demonstrated as an efficient treatment method for various cancers. However, the availability of companion therapy response biomarkers are limited (some of these biomarkers are included in our cancer biomarker database). Wide adoption of combination therapy as a method for cancer treatment may warrants a need for the discovery and validation of new biomarkers associated with combination therapy.

Identification of new biomarkers and availability of large number biomarkers may result in the development of theranostics for most of the cancer types. A cancer biomarker database that contains comprehensive and cumulative information on experimental and clinically validated biomarkers, especially companion diagnostic biomarkers and therapeutically relevant biomarkers, may opens up scientific and technical opportunities in developing innovative oncologic theranostics (Rx/Dx) tools.

Sciclips cancer biomarker database contains more than 8700 cancer biomarkers, which are classified into 1) diagnostic biomarkers 2) disease predictive/risk assessment biomarkers 3) drug efficacy/response biomarkers 4) prognostic biomarkers and 5) cancer companion diagnostics biomarker pathway. The biological and molecular functions, biological process associated, chromosomal location, SNPs and protein-protein interaction networks of each biomarker are listed in this database. This comprehensive information will be useful for the validation of existing biomarkers and for the identification and validation of new biomarkers for cancer theranostics. Please follow the link to see the details of cancer biomarker database: http://www.sciclips.com/sciclips/diagnostic-prognostic-cancer-biomarker-main.do